Global Agricultural Monitoring to support adaptation

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1 El cambio de clima y la producción agropecuaria Global Agricultural Monitoring to support adaptation Pascal Kosuth, for the GEOGLAM group and GEO Agriculture Community of Practice Cereal grain production per hectare (Source: SAGE, Univ. of Wisconsin, Madison, Wisc., USA)

2 1. Climate change and its impact on agriculture 1. Measuring; Understanding, Modelling; Forecasting 2. A case study : sanitary risk 2. The GEOGLAM initiative : Global Agricultural Monitoring 2

3 An international effort to understand and forecast climate change IPCC Intergovernmental Panel on Climate Change A two-dimension entity : - scientific (>3000 scientists designed by govts) - and political (intergovernmental) that provides 5 year reports (1990, 1995, 2001, 2007) on our knowledge of climate evolution under various scenarios related with Green House Gaz emission Forecasted impact XII of Foro climate de Expectativas change on del rainfed Sector Agroalimentario cereal potential y Pesquero production 2012 El desafío de alimentar a México y el mundo, Mexico (source 2012 FAO, March 2008) 22-23

4 1. Measuring Temperature and carbon dioxide Globally averaged surface air temperature and carbon dioxide concentration (parts per million by volume) since 1880 (Updated from Karl and Trenberth, 2003)

5 1. Measuring Green House Gaz atmospheric concentrations Changes in atmospheric concentration of carbon dioxide, methane, and nitrous oxide since 1000 A.D. (from IPCC, 2001)

6 1. Measuring cropping calendar evolution (ex. Wine harv. date - Rhône, Fr) One month advance in 50 years, Alcohol rate increase; migration northward

7 2. Understanding and Modeling the Climate System A complex system : Components : sun, atmosphere, ocean, cryosphere, land, biosphere, man Retroactions : ocean inertia, icesheet inertia, vegetation response Uncertainties : evolution of Green House Gaz emission rates; role of clouds and aerosols A rigorous scientific approach for modelling scenarios : various modelling groups

8 2. Understanding and Modeling the Climate System (1) A Global (planet scale) approach : Grid representation of the planet Submodels per component (atmosph., ocean, cryosphere, land and biosphere) Coupling sub-models (retroactions) Forcing scenarios

9 2. Understanding and Modeling the Climate System (1) A Global (planet scale) approach : Grid representation of the planet Submodels per component (atmosph., ocean, cryosphere, land and biosphere) Coupling sub-models (retroactions) Forcing scenarios (2) Regional approaches : Fine scale models Regional scenarios Modelling past period and future period

10 3. Forecasting impacts : mean temperature increase

11 Climate Projections (NCAR USA; CSIRO-Australia) with Green House Gaz emission scenarios (IPCC A2 scenario) Temperature Mexico Temperature increase +2 C Precipitation decrease -100mm/year Source : IFPRI 2009 Changement climatique, impact sur l agriculture et coûts d adaptation

12 Climate Projections (NCAR USA; CSIRO-Australia) with Green House Gaz emission scenarios (IPCC A2 scenario) Precipitations Mexico Temperature increase +2 C Precipitation decrease -100mm/year Source : IFPRI 2009 Changement climatique, impact sur l agriculture et coûts d adaptation

13 3. Forecasting impacts : rainfall variability Forecasted impact XII of Foro climate de Expectativas change on del rainfed Sector Agroalimentario cereal potential y Pesquero production 2012 El desafío de alimentar a México y el mundo, Mexico (source 2012 FAO, March 2008) 22-23

14 3. Forecasting impacts : crop production Forecasted impact XII of Foro climate de Expectativas change on del rainfed Sector Agroalimentario cereal potential y Pesquero production 2012 El desafío de alimentar a México y el mundo, Mexico (source 2012 FAO, March 2008) 22-23

15 1. Climate change and its impact on agriculture 1. Measuring; Understanding, Modelling; Forecasting 2. A case study : sanitary risk 2. The GEOGLAM initiative : Global Agricultural Monitoring 15

16 3. Forecasting impacts : sanitary risk Mapping the effects of climate change on bluetongue transmission in Europe H. Guis, C. Caminade, C. Calvete, A. Morse, F. Roger, M. Baylis SExES, CIRAD, Montpellier, 17/09/2009

17 Bluetongue: an emerging disease in Europe Bluetongue: arboviral disease of ruminants transmitted by some species of Culicoides Historically a tropical disease - Historical distribution: 35 S - 40 N Massive emergence in Europe in the last 10 years in Europe: - Different (9) serotypes of virus - Different vectors, both exotic and local 40 N 35 S Presence of Culicoides imicola before 1998 Area of circulation of BTV before 1998 Adapted from Purse B.V. et al Vectors? Obsoletus gp? BTV 8, 6, 1 Cattle & sheep C. imicola BTV1,2,4,9,16 sheep Source: European Commission, May 2009 Suspected links with Climate Change : are we able to identify? to forecast?

18 Climate and vector-borne diseases Vector-borne disease (disease transmitted by blood-sucking arthropod): 3 actors, all under influence of climate Climate affects: Survival Replication rate pathogen host Growth rate Survival Distribution Life habits Development rate Survival Activity (thus contact rates) Length of gonotrophic cycle Vector competence vector Climate? Temperature Humidity/Rain Wind Soil moisture Variability???

19 Modelling installation risk : R0 indicator R0 models for vector-borne diseases : risk of installation What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population?

20 Modelling installation risk : R0 indicator R0 models for vector-borne diseases : risk of installation What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population?

21 Modelling installation risk : R0 indicator R0 models for vector-borne diseases : risk of installation What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population?

22 Modelling installation risk : R0 indicator R0 models for vector-borne diseases What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population? 1 1 Vector contamination Duration of viraemia in host (1/r) Ratio vector/host (m) Biting rate (a)

23 Modelling installation risk : R0 indicator R0 models for vector-borne diseases What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population? Vector contamination Duration of viraemia in host (1/r) Ratio vector/host (m) Biting rate (a) 2 Vector competence Competence (c) Survival (p) during the Length of extrinsic incubation period (n)

24 Modelling installation risk : R0 indicator R0 models for vector-borne diseases What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population? Vector contamination Duration of viraemia in host (1/r) Ratio vector/host (m) Biting rate (a) 2 Vector competence Competence (c) Survival (p) during the Length of extrinsic incubation period (n) 3 Transmission to new host Survival (p) Biting rate (a)

25 Modelling installation risk : R0 indicator R0 models for vector-borne diseases What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population? Vector contamination Duration of viraemia in host (1/r) Ratio vector/host (m) Biting rate (a) 2 Vector competence Competence (c) Survival (p) during the Length of extrinsic incubation period (n) 3 Transmission to new host Survival (p) Biting rate (a) R0 m: ratio vectors to host a: biting rate n m a² b p r ln( p) b: vector competence p: daily survival rate n: extrinsic incubation period r:1/duration of viraemia in host

26 Modelling installation risk : R0 indicator R0 models for vector-borne diseases What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population? Statistical models of presence and abundance of C. imicola and C. 1 obsoletus 2 3 (temperature & precipitation related) (C. Calvete) n m a² b p R0 r ln( p) m: ratio vectors to host a: biting rate b: vector competence p: daily survival rate n: extrinsic incubation period r:1/duration of viraemia in host

27 Modelling installation risk : R0 indicator R0 models for vector-borne diseases What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population? 1 Lab based 2 3 studies : relation between vector parameters and Temperature n m a² b p R0 r ln( p) m: ratio vectors to host a: biting rate b: vector competence p: daily survival rate n: extrinsic incubation period r:1/duration of viraemia in host

28 Modelling installation risk : R0 indicator R0 models for vector-borne diseases What is the risk of secondary cases arising from the introduction of an infected individual in a fully susceptible population? n m a² b p R0 r ln( p) m: ratio vectors to host a: biting rate b: vector competence p: daily survival rate n: extrinsic incubation period r:1/duration of viraemia in host Can we map risk indicators from environmental data and climatic data (Temp., precipitations)

29 R0 past anomalies (Observed climate) R0 relative anomalies (relative to climatology) Global increasing north-south gradient Northwest at risk

30 R0 past anomalies (Observed climate) R0 relative anomalies (relative to climatology) Global increasing north-south gradient Northwest at risk 15 years before virus was introduced (2006)

31 Future anomalies ( ) : SRESA1B R0 relative anomalies in future and trends Global trends: positive anomalies, especially in northern Europe

32 Conclusion (1) Climate change strongly affects not only the water cycle and carbon cycle (2) Climate change strongly affects biosphere and human activity (3) There is a strong need for an international effort to monitor, understand and forecast climate change and its impact (4) There is a need for political commitment to mitigation and adaptation efforts Forecasted impact XII of Foro climate de Expectativas change on del rainfed Sector Agroalimentario cereal potential y Pesquero production 2012 El desafío de alimentar a México y el mundo, Mexico (source 2012 FAO, March 2008) 22-23

33 1. Climate change and its impact on agriculture Measuring Understanding, modelling Forecasting Acting for mitigation 2. The GEOGLAM initiative : Global Agricultural Monitoring 33

34 SORCE Envisat Aura/Aqua/Terra QuikScat Sage IKONOS CBERS SeaWiFS SeaWinds SPOT 4, 5 Orbview 2, 3 GEO-GLAM SPIN-2 TRMM DMC ACRIMSAT EROS A1 GLOBAL ERBS AGRICULTURAL MONITORING Radarsat ALOS QuickBird Pascal Kosuth, Toms-EP Grace for the GEOGLAM group and Landsat 7 UARS GEO Agriculture Jason Community of Practice GEO Secretariat Cereal grain production per hectare (Source: SAGE, Univ. of Wisconsin, Madison, Wisc., USA)

35 GEO Secretariat 1. The GEO-GLAM Initiative : objectives To reinforce the international community s capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural production at national, regional and global scales. SORCE Envisat Aura/Aqua/Terra QuikScat Sage IKONOS CBERS SeaWiFS SPOT 4, 5 SPIN-2 SeaWinds Orbview 2, 3 TRMM DMC ACRIMSAT ERBS Radarsat ALOS EROS A1 Grace QuickBird UARS Toms-EP Landsat 7 Jason Earth Observation Agronomy Agro-economy Cultivated area / crop type area Meteorology Cereal grain production per hectare (Source: SAGE, Univ. of Wisconsin, Madison, Wisc., USA) Crop yield forecast 35

36 1. The GEO-GLAM Initiative : background (G ) 36

37 1. The GEO-GLAM Initiative : background (G ) G20 Final Declaration (nov. 2011) 44. We commit to improve market information and transparency in order to make international markets for agricultural commodities more effective. To that end, we launched: The "Agricultural Market Information System" (AMIS) in Rome on September 15, 2011, to improve information on markets...; The "Global Agricultural Geo-monitoring Initiative" (GEOGLAM) in Geneva on September 22-23, 2011, to coordinate satellite monitoring observation systems to enhance crop production projections... 37

38 4. Linking and GEOGLAM International effort to increase information availability, quality and transparency Supply Price Demand Policies Finance Trade Stocks AMIS 38

39 Normalized difference vegetation index over Pakistan August 3, 2011 (FAO)

40 2. The GEO-GLAM Initiative : actions and outputs Action 1. Global and regional agricultural monitoring Systems Harmonizing, connecting, strengthening inter-comparing existing systems, disseminating information. Action 2. Strengthening national capacities for agric. monitoring capacity development for the use of EO, experience sharing, research Action 3. At risk regions and countries agricultural monitoring Improving monitoring methods, tools and systems for vulnerable agricultural systems Action 4. Global Earth observation system of systems for agricult. Developing a coordinated operational system : satellite and in-situ ; Actions 5 and 6 : Research Coordination + Inform. dissemination Long term commitment. Full and open data policy. 40

41 2. The GEO-GLAM Initiative : actions and outputs Deliverable 1 : Access to Earth Observation data for agriculture monitoring Deliverable 2 : Access to Meteorological data and forecasts Deliverable 3 : Cultivated areas, crop-type distribution, crop yield forecasts Deliverable 4 : Improved monitoring methods Deliverable 5 : Strengthened national agricultural monitoring capacities Deliverable 6 : Dissemination of data to stakeholders; Deliverable 7 : A sustained Earth observation system of systems for agricultural monitoring, 41

42 3. The GEO-GLAM Initiative : Components Agricultural Expertise Meteorological Expertise and Info Earth Observations Satellite / Ground Data / Models 4 1 Enhancing 2 National 3 Agriculture Global Agricultural Monitoring Systems Capacity for Agricultural Monitoring Monitoring for countries at risk Coordinated Satellite and In-Situ Earth Observations 5 6 Operational Research and Development Data/Techniques/Methods/Best Practices Data, products and information Dissemination Condition/Area/ Yield / Statistics Public Govts AMIS FAO Stat 42

43 5. The GEO-GLAM Initiative : Status and agenda Key actors of the Initiative : International entities : Operational actors : GEO, FAO, WMO, CEOS, CGIAR GEO Agriculture Community of Practice Regional and National organisations Private actors, NGOs

44 5. The GEO-GLAM Initiative : Status and agenda Jan.- May 2011 : Initial design in the framework of G20 priorities June 2011 : Adoption by G20 Agriculture Action Plan Sept : International GEO-GLAM meeting at GEO Geneva Nov : G20 Final Declaration (GEO-GLAM art. 44) Nov : Presentation at GEO VIII Plenary (90 countries, 60 inst.) Nov.11 Feb : March June 2012 : July 2012 on : Detailed action plan and budget + Governance scheme + Linkage with AMIS Governance definition and approval Linkage with G20 Mexican presidency Securing funds for implementation Informing national and international actors Implementation Intercomparison of global products; JECAM; El EO desafío data access; de alimentar Capacity a México building y el modules mundo, Mexico 2012 March

45 GEO-GLAM GLOBAL AGRICULTURAL MONITORING Thank you for your attention 45

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